Vibration and infrared thermography based multiple fault diagnosis of bearing using deep learning

Mian, Tauheed; Choudhary, Anurag; Fatima, Shahab

doi:10.1080/10589759.2022.2118747

Cited by 33 publications

(4 citation statements)

References 57 publications

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“…Deep learning and traditional machine learning are similar in data preprocessing. The core difference lies in the feature extraction process, where deep learning does not require manual extraction and the extraction process is performed by a machine [20][21][22][23]. Although deep learning can learn the features of patterns automatically and achieve high recognition accuracy, the prerequisite is that amounts of data are provided.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Evaluation of the Degradation Process of Vibration Performance for Machine Tool Spindle Bearings

Liang

Zhang

Cui

et al. 2023

Sensors

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Real-time condition monitoring and fault diagnosis of spindle bearings are critical to the normal operation of the matching machine tool. In this work, considering the interference of random factors, the uncertainty of the vibration performance maintaining reliability (VPMR) is introduced for machine tool spindle bearings (MTSB). The maximum entropy method and Poisson counting principle are combined to solve the variation probability, so as to accurately characterize the degradation process of the optimal vibration performance state (OVPS) for MTSB. The dynamic mean uncertainty calculated using the least-squares method by polynomial fitting, fused into the grey bootstrap maximum entropy method, is utilized to evaluate the random fluctuation state of OVPS. Then, the VPMR is calculated, which is used to dynamically evaluate the failure degree of accuracy for MTSB. The results show that the maximum relative errors between the estimated true value and the actual value of the VPMR are 6.55% and 9.91%, and appropriate remedial measures should be taken before 6773 min and 5134 min for the MTSB in Case 1 and Case 2, respectively, so as to avoid serious safety accidents that are caused by the failure of OVPS.

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Section: Introductionmentioning

confidence: 99%

Dynamic Evaluation of the Degradation Process of Vibration Performance for Machine Tool Spindle Bearings

Liang

Zhang

Cui

et al. 2023

Sensors

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“…After the extraction of thermal images, a region of interest is selected and converted it into a grayscale image using the scale of [0,255]. This is done because the backstory of the originally captured image may be miss leads to the process (Mian et al, 2022c). The uncertainty in the adjustment of hyperparameters creates a time-consuming and tedious scenario.…”

Section: Extraction Of the Thermal Imagesmentioning

confidence: 99%

Nature‐inspired artificial bee colony‐based hyperparameter optimization of CNN for anomaly detection in induction motor

et al. 2023

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The Induction Motor (IM) is one of the most frequently used prime movers in most industrial and transportation systems. The motor's stable and safe operation directly influences the secure and reliable operation of such prime movers. Developing an intelligent fault diagnosis system for such motors is very significant. This paper presents an intelligent fault diagnosis method based on the improved functionality of a Convolutional Neural Network (CNN) through its hyperparameter optimization using a nature‐inspired Artificial Bee Colony Optimization (ABCO) algorithm. The proposed diagnostic method introduces and analyses various possible mechanical and electrical faults in the IM. The validation of the proposed method is presented with three different modalities, including vibration, acoustic, and infrared thermography, with their comparative performance analysis. Vibration and acoustic‐based detection are done with time‐frequency scalograms using Constant Q Transform (CQT), which provides enhanced time resolution for lower and higher frequencies. The obtained result indicates that the infrared thermography‐based anomaly detection outperforms the vibration and acoustic‐based diagnosis with 100% classification accuracy. The results signify the potential to diagnose different mechanical and electrical faults in IM with substantial reliability and robustness.

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“…[ 23 ] proposes an innovative online detection scheme for diagnosing incipient inter-turn short circuit faults and estimating failure severity in induction motors to provide the motor with a safe operating area by introducing a new mathematical variable based on the discrete wavelet analysis and using a multi-class SVM to carry out the classification function, their work intends to estimate the proportion of faulty turns in the shorted winding. Tauheed Mian et al [ 24 ] examined different bearing failure combinations, including dual and multiple fault circumstances, using two widely used fault diagnosis techniques: non-invasive infrared thermography (IRT) and vibration monitoring in the time-frequency domain by extracting scalograms. They used a CNN network to classify the fault combinations.…”

Section: Related Workmentioning

confidence: 99%

Fault Detection in Induction Motor Using Time Domain and Spectral Imaging-Based Transfer Learning Approach on Vibration Data

Misra

Kumar

Sayyad

et al. 2022

Sensors

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The induction motor plays a vital role in industrial drive systems due to its robustness and easy maintenance but at the same time, it suffers electrical faults, mainly rotor faults such as broken rotor bars. Early shortcoming identification is needed to lessen support expenses and hinder high costs by using failure detection frameworks that give features extraction and pattern grouping of the issue to distinguish the failure in an induction motor using classification models. In this paper, the open-source dataset of the rotor with the broken bars in a three-phase induction motor available on the IEEE data port is used for fault classification. The study aims at fault identification under various loading conditions on the rotor of an induction motor by performing time, frequency, and time-frequency domain feature extraction. The extracted features are provided to the models to classify between the healthy and faulty rotors. The extracted features from the time and frequency domain give an accuracy of up to 87.52% and 88.58%, respectively, using the Random-Forest (RF) model. Whereas, in time-frequency, the Short Time Fourier Transform (STFT) based spectrograms provide reasonably high accuracy, around 97.67%, using a Convolutional Neural Network (CNN) based fine-tuned transfer learning framework for diagnosing induction motor rotor bar severity under various loading conditions.

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Vibration and infrared thermography based multiple fault diagnosis of bearing using deep learning

Cited by 33 publications

References 57 publications

Dynamic Evaluation of the Degradation Process of Vibration Performance for Machine Tool Spindle Bearings

Dynamic Evaluation of the Degradation Process of Vibration Performance for Machine Tool Spindle Bearings

Nature‐inspired artificial bee colony‐based hyperparameter optimization of CNN for anomaly detection in induction motor

Fault Detection in Induction Motor Using Time Domain and Spectral Imaging-Based Transfer Learning Approach on Vibration Data

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